DDA-Net: Unsupervised cross-modality medical image segmentation via dual domain adaptation

Abstract

Background and objective: Deep convolutional networks are powerful tools for single-modality medical image segmentation, whereas generally require semantic labelling or annotation that is laborious and time-consuming. However, domain shift among various modalities critically deteriorates the performance of deep convolutional networks if only trained by single-modality labelling data.

Methods: In this paper, we propose an end-to-end unsupervised cross-modality segmentation network, DDA-Net, for accurate medical image segmentation without semantic annotation or labelling on the target domain. To close the domain gap, different images with domain shift are mapped into a shared domain-invariant representation space. In addition, spatial position information, which benefits the spatial structure consistency for semantic information, is preserved by an introduced cross-modality auto-encoder.

Results: We validated the proposed DDA-Net method on cross-modality medical image datasets of brain images and heart images. The experimental results show that DDA-Net effectively alleviates domain shift and suppresses model degradation.

Conclusions: The proposed DDA-Net successfully closes the domain gap between different modalities of medical image, and achieves state-of-the-art performance in cross-modality medical image segmentation. It also can be generalized for other semi-supervised or unsupervised segmentation tasks in some other field.

Keywords: Cross-modality; Domain adaptation; Medical image; Segmentation; Unsupervised learning.